class_docstr: An implementation of Squeeze-and-Excitation ResNet Block.
  
  Arguments
  ----------
  in_channels : int
      Number of input channels.
  out_channels : int
      The number of output channels.
  stride : int
      Factor that reduce the spatial dimensionality
  downsample : torch function
      A function for downsample the identity of block when stride != 1
  activation : torch class
      A class for constructing the activation layers.
  
  Example
  -------
  >>> inp_tensor = torch.rand([1, 64, 80, 40])
  >>> layer = SEBasicBlock(64, 64, stride=1)
  >>> out_tensor = layer(inp_tensor)
  >>> out_tensor.shape
  torch.Size([1, 64, 80, 40])
docstr: Intermediate step. Processes the input tensor x
  and returns an output tensor.
docstr_tok: ["Intermediate","step",".","Processes","the","input","tensor","x","and","returns","an","output","tensor","."]
code: def forward(self, x):
      """Intermediate step. Processes the input tensor x
      and returns an output tensor.
      """
      residual = x
  
      out = self.bn1(x)
      out = self.activation(out)
      out = self.conv1(out)
  
      out = self.bn2(out)
      out = self.activation(out)
      out = self.conv2(out)
  
      out = self.bn3(out)
      out = self.activation(out)
      out = self.conv3(out)
  
      out = self.se(out)
  
      if self.downsample is not None:
          residual = self.downsample(x)
  
      out += residual
  
      return out
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from speechbrain.nnet.normalization import BatchNorm1d
  from speechbrain.nnet.linear import Linear
label_desc: Use image node_id 2 for calling the SEBasicBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out